timestamp <- Sys.time()
library(caret)
library(plyr)
library(recipes)
library(dplyr)

model <- "pythonKnnReg"



#########################################################################


library(caret)
library(plyr)
library(recipes)
library(dplyr)
set.seed(1)
training <- SLC14_1(30)
testing <- SLC14_1(100)
trainX <- training[, -ncol(training)]
trainY <- training$y

rec_reg <- recipe(y ~ ., data = training) %>%
  step_center(all_predictors()) %>%
  step_scale(all_predictors()) 
testX <- trainX[, -ncol(training)]
testY <- trainX$y 

rctrl1 <- trainControl(method = "cv", number = 3, returnResamp = "all")
rctrl2 <- trainControl(method = "LOOCV")
rctrl3 <- trainControl(method = "none")
rctrlR <- trainControl(method = "cv", number = 3, returnResamp = "all", search = "random")

set.seed(849)
test_reg_cv_model <- train(trainX, trainY, 
                           method = "pythonKnnReg", 
                           trControl = rctrl1,
                           preProc = c("center", "scale"))
test_reg_pred <- predict(test_reg_cv_model, testX)

# set.seed(849)
# test_reg_cv_form <- train(y ~ ., data = training, 
#                           method = "pythonKnnReg", 
#                           trControl = rctrl1,
#                           preProc = c("center", "scale"))
# test_reg_pred_form <- predict(test_reg_cv_form, testX)

set.seed(849)
test_reg_rand <- train(trainX, trainY, 
                       method = "pythonKnnReg", 
                       trControl = rctrlR,
                       tuneLength = 4,
                       preProc = c("center", "scale"))

set.seed(849)
test_reg_loo_model <- train(trainX, trainY, 
                            method = "pythonKnnReg",
                            trControl = rctrl2,
                            preProc = c("center", "scale"))
 
set.seed(849)
test_reg_none_model <- train(trainX, trainY, 
                             method = "pythonKnnReg", 
                             trControl = rctrl3,
                             tuneGrid = data.frame(n_neighbors = 3, weights = "uniform", algorithm = "auto", 
                                                   leaf_size = 30, metric = "minkowski", p = 1),
                             preProc = c("center", "scale"))
test_reg_none_pred <- predict(test_reg_none_model, testX)

#########################################################################

tests <- grep("test_", ls(), fixed = TRUE, value = TRUE)

sInfo <- sessionInfo()
timestamp_end <- Sys.time()

save(list = c(tests, "sInfo", "timestamp", "timestamp_end"),
     file = file.path(getwd(), paste(model, ".RData", sep = "")))

if(!interactive())
   q("no")

